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A Simple Algorithm for Semi-supervised Learning with Improved Generalization Error Bound

2020-03-02 | |  61 |   43 |   0

Abstract

In this work, we develop a simple algorithm for semi-supervised regression. The key idea is to use the top eigenfunctions of integral operator derived from both labeled and unlabeled examples as the basis functions and learn the prediction function by a simple linear regression. We show that under appropriate assumptions about the integral operator, this approach is able to achieve an improved regression error bound better than existing bounds of supervised learning. We also verify the e?ectiveness of the proposed algorithm by an empirical study.

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